In many modern data analysis scenarios the first and most urgent task consists of reducing the redundancy in high dimensional input spaces. A method is presented that quantifies the discriminative power of the input features in a fuzzy model. A possibilistic information measure of the model is defined on the basis of the available fuzzy rules and the resulting possibilistic information gain, associated with the use of a given input dimension, characterizes the input feature’s discriminative power. Due to the low computational expenses derived from the use of a fuzzy model, the proposed possibilistic information gain generates a simple and efficient algorithm for the reduction of the input dimensionality, even for high dimensional cases. As real-world example, the most informative electrocardiographic measures are detected for an arrhythmia classification problem.
CITATION STYLE
Silipo, R., & Berthold, M. R. (1999). Discriminative power of input features in a fuzzy model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1642, pp. 87–98). Springer Verlag. https://doi.org/10.1007/3-540-48412-4_8
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